Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchical Feature Selection with Recursive Regularization
Authors: Hong Zhao, Pengfei Zhu, Ping Wang, Qinghua Hu
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public datasets demonstrate the effectiveness of the proposed algorithm. |
| Researcher Affiliation | Academia | 1Tianjin University, China 2Lab of Granular Computing, Minnan Normal University, China |
| Pseudocode | Yes | Algorithm 1 Hierarchical Feature Selection with Recursive Regularization (Hi FSRR) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release, or mention of code in supplementary materials) for the source code. |
| Open Datasets | Yes | Two protein tasks include: F194 [Wei et al., 2015] and DD [Ding and Dubchak, 2001]. Four image tasks include: CLEF [Dimitrovski et al., 2011], CIFAR-100 [Krizhevsky and Hinton, 2009], PASCAL Visual Object Classes (VOC) [Everingham et al., 2010], and Scene UNderstanding (SUN) [Xiao et al., 2010]. |
| Dataset Splits | Yes | We select features on training sets and test them on test sets using 10fold cross validation. |
| Hardware Specification | Yes | All experiments are executed on an Intel Core i7-3770 running at 3.40 GHz with 12.0 GB memory and 64-bit Windows 7 operating system. |
| Software Dependencies | No | The paper mentions using SVM for classification, but does not provide specific software dependencies with version numbers (e.g., the specific SVM library and its version). |
| Experiment Setup | Yes | In the experiments, we set λ = 1, β = 1, and α = 1 for the CLEF dataset, and set λ = 10, β = 0.1, and α = 0.1 for the other datasets. |